Systems and Methods of Address Book Management

1. A method comprising: receiving, by a server from a communication device, address book data comprising a received contact associated with a contact identifier; retrieving, from a database, data stored in an address book, the data comprising information associated with the received contact; when a contact associated with the contact identifier is identifiable from the data, generating a merged contact comprising the information for the received contact and information for the contact, and sending, by the server to the communication device, at least some information for the merged contact; and when two or more contacts associated with the contact identifier are identifiable from the data, sending, by the server to the communication device, at least some information associated with one of the two or more contacts having a highest confidence value, wherein, when a respective contact is created by a user or imported from an external source, a confidence value for the respective contact is generated based on whether the respective contact was created by the user, or imported from the external source, and wherein the confidence value is indicative of a number of different users that contribute to create a node, via a user contact, with the respective contact in a contact network.

2. The method of claim 1 , wherein, when the two or more contacts associated with the contact identifier are identifiable from the data, the method further comprises: applying a machine learning classifier to the contact in the address book data to determine a probability that the contact in the address book data and the one of the two or more contacts in the data are a same contact; and determining that the contact in the address book data and the one of the two or more contacts in the address book data are the same contact when the probability equals or exceeds a threshold value.

3. The method of claim 1 , wherein the merged contact is generated by a method comprising clustering the contact in the address book data and the contact in the address book by applying a clustering algorithm which uses a distance function to group the contact in the address book data and the contact in the address book based on a distance between the contact in the address book data and the contact in the address book.

4. The method of claim 1 , wherein the contact associated with the contact identifier, which is identifiable from the data, is determined by: selecting the contact from among a plurality of contacts in the address book data; generating a list of multiple candidate contacts from among a plurality of contacts in the data; and comparing the contact with the candidate contacts.

5. The method of claim 4 , wherein the comparing comprises: matching a name in the contact to a name in at least one of the candidate contacts; matching a source and an ID associated with the contact to a source and an ID associated with at least one of the candidate contacts; or matching an origin and the ID associated with the contact to an origin and the ID associated with at least one of the candidate contacts.

6. The method of claim 1 , wherein: the data further comprises at least one confidence value for each contact in the data; and when the two or more contacts associated with the contact identifier are identifiable from the data, selecting one of the two or more contacts having the highest confidence value as a same contact as the contact in the address book data.

7. The method of claim 1 , further comprising: identifying information associated with the contact from a public data source by searching the public data source for the information related to the contact in the address book data; adding the information from the public data source to the address book data when the information related to the contact in the address book data is found; and storing the address book data with the information in the database.

8. The method of claim 7 , wherein the public data source is at least one of stored in the database, comprises directory data, or comprises crowdsourced data.

9. The method of claim 7 , wherein the generating comprises clustering the information and the contact in the address book by applying a clustering algorithm which uses a distance function to group the information and the contact in the address book based on a distance between the information and the contact in the address book.

10. The method of claim 1 , wherein the one of the two or more contacts having the highest confidence value is selected by a method comprising: clustering two or more separate groups of the two or more contacts by applying a clustering algorithm which uses a distance function to group the two or more contacts with one another based on a distance between the two or more contacts; and selecting one of the two or more separate groups.

11. A system comprising: a memory comprising instructions; and one or more processors configured to execute the instructions to: receive address book data comprising a contact associated with a contact identifier; retrieve, from a database, a data stored in an address book, the data comprising information associated with the contact; when a contact associated with the contact identifier is identifiable from the data, generate a merged contact comprising the information for the received contact and information for the contact to the data in the database, and send, to a communication device, at least some information for the merged contact; and when two or more contacts associated with the contact identifier are identifiable from the data, send, to the communication device via a network, at least some information associated with one of the two or more contacts having a highest confidence value, wherein, when a respective contact is created by a user or imported from an external source, a confidence value for the respective contact is generated based on whether the respective contact was created by the user, or imported from the external source, and wherein the confidence value is indicative of a number of different users that contribute to create a node, via a user contact, for the respective contact in a contact network.

12. The system of claim 11 , wherein when the two or more contacts associated with the contact identifier are identifiable from the data, the one or more processors are configured to: apply a machine learning classifier to the contact in the address book data to determine a probability that the contact in the address book data and the one of the two or more contacts in the data are a same contact; and determine that the contact in the address book data and the one of the two or more contacts in the data are the same contact when the probability equals or exceeds a threshold value.

13. The system of claim 11 , wherein the merged contact is generated by instructions to cluster the contact in the address book data and the contact in the address book with a clustering algorithm which uses a distance function to group the contact in the address book data and the contact in the address book based on a distance between the contact in the address book data and the contact in the address book.

14. The system of claim 11 , wherein the contact associated with the contact identifier, identifiable from the data, is determined by the one or more processors being configured to: select the contact from among a plurality of contacts in the address book data; generate a list of multiple candidate contacts from among a plurality of contacts in the data; and compare the contact with the candidate contacts.

15. The system of claim 14 , wherein the one or more processors are configured to compare the contact with the candidate contacts by: matching a name in the contact to a name in at least one of the candidate contacts; matching a source and an ID associated with the contact to a source and an ID associated with at least one of the candidate contacts; or matching an origin and the ID associated with the contact to an origin and the ID associated with at least one of the candidate contacts.

16. The system of claim 11 , wherein: the data further comprises at least one confidence value for each contact in the data; and when the two or more contacts associated with the contact identifier are identifiable from the data, the one or more processors are further configured to select the one of the two or more contacts having the highest confidence value as a same contact to the contact in the address book data.

17. The system of claim 11 , wherein the one or more processors are further configured to: identify information associated with the contact from a public data source by searching the public data source for the information related to the contact in the address book data; add the information from the public data source to the address book data when the information related to the contact in the address book data is found; and store the address book data with the information in the database.

18. The system of claim 11 , wherein the one or more processors are configured to generate the merged contact by clustering the information and the contact in the address book by applying a clustering algorithm which uses a distance function to group the information and the contact in the address book based on a distance between the information and the contact in the address book.

19. The system of claim 11 , wherein the one or more processors are configured to select the one of the two or more contacts having the highest confidence value by: clustering two or more separate groups of the two or more contacts by applying a clustering algorithm which uses a distance function to group the two or more contacts with one another based on a distance between the two or more contacts; and selecting one of the two or more separate groups.

20. A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method comprising: receiving, by a server from a communication device, address book data comprising a contact associated with a contact identifier; retrieving, from a database, address book data comprising information associated with the contact; when a contact associated with the contact identifier is identifiable from the address book data, generating a merged contact comprising the information for the contact and information for the contact to the address book data, and sending, by the server to the communication device, at least some information for the merged contact; and when two or more contacts associated with the contact identifier are identifiable from the address book data, sending, by the server to the communication device, at least some information associated with one of the two or more contacts having a highest confidence value, wherein, when a respective contact is created by a user or imported from an external source, a confidence value for the respective contact is generated based on whether the respective contact was created by the user, or imported from the external source, and wherein the confidence value is indicative of a number of different users that contribute to create a node, via a user contact, for the respective contact in a contact network.